from __future__ import division
import sys

import cvxopt
import numpy as np
from pylab import *
import math

from cvxpy import *

# Taken from CVX website http://cvxr.com/cvx/examples/
# Figure 6.2: Penalty function approximation
# Ported from cvx matlab to cvxpy by Misrab Faizullah-Khan
# Original comments below

# Section 6.1.2
# Boyd & Vandenberghe "Convex Optimization"
# Original by Lieven Vandenberghe
# Adapted for CVX Argyris Zymnis - 10/2005
#
# Comparison of the ell1, ell2, deadzone-linear and log-barrier
# penalty functions for the approximation problem:
#       minimize phi(A*x-b),
#
# where phi(x) is the penalty function

# Generate input data
m, n = 100, 30
A = cvxopt.normal(m,n) #np.random.randn(m,n)
b = cvxopt.normal(m,1) #np.random.randn(m,1)

# l-1 approximation
x1 = Variable(n)
objective1 = Minimize( norm(A*x1-b, 1) )
p1 = Problem(objective1, [])
#p1 = Problem(Minimize( norm(A*x1-b, 1), []))

# l-2 approximation
x2 = Variable(n)
objective2 = Minimize( norm(A*x2-b, 2) )
p2 = Problem(objective2, [])

# deadzone approximation
# minimize sum(deadzone(Ax+b,0.5))
# deadzone(y,z) = max(abs(y)-z,0)
def deadzone(y,z):
	return pos(abs(y)-z)

dz = 0.5
xdz = Variable(n)
objective3 = Minimize( sum_entries( deadzone(A*xdz+b, dz) ) )
p3 = Problem(objective3, [])

# Solve the problems
p1.solve()
p2.solve()
p3.solve()

# Plot histogram of residuals
range_max=2.0
#rr = np.arange(-range_max, range_max, 1e-2)
rr = np.linspace(-2, 3, 20)



# l-1 plot
subplot(3, 1, 1)
n, bins, patches = hist(A*x1.value-b, 50, range=[-2, 2])
plot(bins, np.abs(bins)*35/3, '-') # multiply by scaling factor for plot
ylabel('l-1 norm')
title('Penalty function approximation')

# l-2 plot
subplot(3, 1, 2)
n, bins, patches = hist(A*x2.value-b, 50,  range=[-2, 2])
plot(bins, np.power(bins, 2)*2, '-')
ylabel('l-2 norm')

# deadzone plot
subplot(3, 1, 3)
n, bins, patches = hist(A*xdz.value+b, 50, range=[-2, 2])
zeros = np.array([0 for x in bins])
plot(bins, np.maximum((np.abs(bins)-dz)*35/3, zeros), '-')
ylabel('deadzone')

show()
